CN113225226B - Cloud native system observation method and system based on information entropy - Google Patents
Cloud native system observation method and system based on information entropy Download PDFInfo
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Abstract
The invention relates to a cloud primary system observation method and system based on information entropy, wherein the method comprises the following steps: constructing a system topological graph comprising each component node of the cloud native system; and observing the operation condition of each component node in the topological graph of the system through the observation index ARO. Compared with the prior art, the observation index ARO is based on the alarm log and the information entropy, more aggregated information can be provided for an observer from the system view angle, the actual operation condition of the system can be more effectively displayed by multi-dimensional consideration from the global and local angles through the combination of the global entropy, the node importance factor and the component entropy, and meanwhile, the dynamic change can be realized along with the operation of the cloud native system, the artificial weight setting is not needed, the automation is realized, and the efficiency is high.
Description
Technical Field
The invention relates to the field of cloud primitive, in particular to a cloud primitive system observation method and system based on information entropy.
Background
The development of service and the continuous evolution of various technologies such as containerized arrangement, micro-service framework, service mesh and the like provide technical support for distributed services, but the micro-service is not enough to be constructed, and for a complete technical system, except for development, strong support needs to be provided for operation and maintenance. With the continuous evolution of micro-service architecture, the number of applications and services is increasing, the calling relationship is more and more complex, and the system deployment situation under the micro-service architecture cannot be described through a static architecture diagram, so from the viewpoint of operation and maintenance, the importance of maintaining observability is increasing. In the whole cloud native system, observability is the same as that of a platform layer formed by an operating system and an underlying network provider, and plays an important role.
The change is the essence of the microservice and is the only unchangeable criterion in the design and development of the application system. The constituent elements, dependency relationship, flow distribution, external boundaries and the like of the micro-service cluster all change with time, and although the micro-service technology reduces the difficulty of coping with the changes, the operation and maintenance team still needs to clearly know the operation condition of the system, which is the appeal of the micro-service system to observability. Observability provides the ability to cross microservice boundaries by first observing and background analyzing application data or management platform data and then visually displaying the current state of the system through a highly visualized system.
According to different observed data, observability is generally considered from three data of logs, indexes and call chains. The log describes discrete events that are not continuous; the index is a logic metering unit, reflects the state of the related index in a period of time, and has the additivity; a call chain is commonly referred to in the monitoring arts as a distributed call chain, which refers to processing information within the scope of a single request. For a large number of components such as entities, services and business systems existing in the cloud native system, logs record the operating conditions of the components, wherein the alarm logs can reflect fault information of the components and are important recording information in the cloud native system.
Depending on the level of observation, discussing observability generally involves infrastructure, tool, and application environment layers. The infrastructure layer is used for monitoring basic indexes including availability of the cloud host, the operating system and the cloud service and providing basic operation and maintenance support capability of a cloud service provider; the tool layer is mainly aimed at arranging tools, along with the continuous promotion of containerization, the monitoring of container arranging ecological tools such as kubernets and meso is more and more diversified, and along with the development of a devops system, the observability of a related calling chain is also the focus of attention at present; observability of the application environment layer mainly refers to observation of middleware components such as application services, databases, message queues, caches and the like.
Since monitoring and system health observation at the infrastructure layer are mostly directly responsible for platform providers, and solutions at the tool layer are basically provided by core products and surrounding ecology thereof, attention should be focused on the application environment layer for developers of micro-services and cloud native applications. The application environment layer is also the most diverse and challenging scenario change because it involves a business system.
In thermodynamics, entropy is a physical quantity representing the degree of disorder of a molecular state, and shannon uses the concept of entropy to describe the uncertainty of a source. In general, what symbol a source sends out is uncertain and can be measured by the probability of its occurrence, and the entropy of information has three properties: monotonicity, nonnegativity and summicity, wherein monotonicity refers to an event with higher occurrence probability, more occurrence opportunities, small uncertainty and lower information content carried by the event; non-negativity means that the probability cannot be less than 0; additive, i.e., a measure of the total uncertainty of the simultaneous presence of multiple random events, is the sum of the measures that can be expressed as the uncertainty of each event. The information entropy formula is as follows:
wherein, X is the information source, X is one of the information sources, and p (X) is the occurrence probability of the information source X.
Analyzing the information entropy formula, it can be seen that the information quantity carried when the event occurs is represented by taking the negative logarithm of the probability, and the information quantities represented by various events are multiplied by the occurrence probability and then summed, so as to represent an expected value of all the information quantities of the whole system. Therefore, the information entropy can be used as a measure of the complexity of the system, and if the system is more chaotic, the types of different conditions are more, and the information entropy of the system is larger. If a system is more stable, the types of the situations are less, and the information entropy is smaller.
The traditional cloud-native observation scheme has the following two problems:
firstly, only basic information of the middleware component, such as CPU usage and network flow, can be monitored, and extraction and display of higher-level semantic information cannot be realized;
secondly, for the user-defined index, the user often depends too much on personal experience of an engineer, and the universality cannot be realized; the custom index is usually static and cannot update the index weight along with the change of a system, so that the weight needs to be reset after a period of time; custom indicators are usually local and cannot take global hierarchical information into account.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a cloud native system observation method and system based on information entropy.
The purpose of the invention can be realized by the following technical scheme:
a cloud native system observation method based on information entropy comprises the following steps:
constructing a system topological graph comprising each component node of the cloud native system;
observing the operation state of each component node in the topological graph of the system through an observation index ARO;
wherein, the calculation formula of the observation index ARO is as follows:
ARO i ＝g*n i *e i
wherein, ARO i Is an observation index of the ith component node, g is the global entropy of the cloud native system, n i Node importance factor, e, for the ith component node i Component entropy for the ith component node;
the observation index ARO makes full use of the information entropy theory, can provide more aggregated information for an observer from a system view angle, the global entropy, the node importance factor and the component entropy fully consider global and local information, the real operation condition of the system can be more effectively displayed, meanwhile, the system can be dynamically changed along with the operation of a cloud primary system, the artificial weight setting is not needed, the automation is realized, and the efficiency is high.
Further, the calculation formula of the global entropy g is as follows:
wherein p is i Generating the probability of an alarm for the ith component node;
the global entropy reflects the overall complexity of the cloud native system in a selected time period, the larger the global entropy is, the more complex the whole cloud native system is, for example, the whole cloud native system has problems, each component node generates various alarms, and the system is very chaotic.
Further, the node importance factor n of the ith component node i The calculation formula of (c) is:
wherein, num i Generating a number of alarms for the ith component node;
the node importance factor represents the frequency of alarm of the component node in a selected time period, and the smaller the frequency is, the larger the node importance factor is.
Further, the calculation formula of the component entropy of the ith component node is as follows:
wherein q is ij Is the probability that the ith component node generates the jth alarm;
the component entropy reflects the complexity degree inside the component node, and the larger the value of the component entropy is, the more chaotic the component node is.
Further, time sequence data of the observation index ARO of each component node is recorded, and a measurement time sequence chart of each component node is generated.
A cloud native system observation system based on information entropy comprises a topology construction module and an index calculation module:
the topology construction module is used for a system topology diagram of the cloud native system, and the system topology diagram comprises each component node of the cloud native system;
the index calculation module comprises a global statistical unit, a weight statistical unit, a local statistical unit and a measurement statistical unit;
the global computing unit is used for computing the global entropy g of the cloud native system;
the weight statistical unit is used for counting the node importance factor n of each component node;
the local statistical unit is used for counting the component entropy e of each component node;
the measurement statistical unit is used for calculating an observation index ARO of each component node, and the calculation formula is as follows:
ARO i ＝g*n i *e i
wherein, ARO i As an observation index of the ith component node, n i Node importance factor, e, for the ith component node i Component entropy for the ith component node.
Further, the calculation formula of the global entropy g is as follows:
wherein p is i Generating an alarm probability for the ith component node;
the global entropy reflects the overall complexity of the cloud native system in a selected time period, the larger the global entropy is, the more complex the whole cloud native system is, for example, the whole cloud native system has problems, each component node generates various alarms, and the system is very chaotic.
Further, the node importance factor n of the ith component node i The calculation formula of (2) is as follows:
wherein, num i Generating a number of alarms for the ith component node for a selected time period;
the node importance factor represents the frequency of alarm generation of the component node in a selected time period, and the smaller the frequency is, the larger the node importance factor is.
Further, the calculation formula of the component entropy of the ith component node is as follows:
wherein q is ij The probability of generating the jth alarm by the ith component node;
the component entropy reflects the complexity degree inside the component node, and the larger the value of the component entropy is, the more chaotic the component node is.
Furthermore, the index calculation module further comprises a time sequence statistics unit, wherein the time sequence statistics unit is used for recording time sequence data of observation indexes ARO of each component node and generating a measurement time sequence diagram of each component node, so that the operation condition of each component node can be observed visually.
Compared with the prior art, the invention has the following beneficial effects:
(1) The method comprises the steps of constructing a system topological graph comprising each component node of a cloud native system; the operating conditions of all component nodes in the topological graph of the observation index ARO are observed, and the observation index ARO is based on the alarm log and the information entropy, so that more aggregated information can be provided for an observer from the system view angle, and the observation is facilitated;
(2) According to the invention, through the combination of the global entropy, the node importance factor and the component entropy, multi-dimensional consideration is carried out from the perspective of global and local, and the real operation condition of the system can be more effectively displayed;
(3) The global entropy, the node importance factor and the component entropy can be dynamically changed along with the operation of a cloud native system, manual weight setting is not needed, automation is realized, efficiency is high, the method does not depend on human subjective experience, and the method is high in objectivity and accuracy.
Drawings
FIG. 1 is a schematic structural view of the present invention;
FIG. 2 is a supply firewall alarm log;
fig. 3 is a proxy server alarm log.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. The present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the scope of the present invention is not limited to the following embodiments.
Example 1
An information entropy-based cloud native system observation method, as shown in fig. 1, includes:
1) Constructing a system topological graph comprising each component node of the cloud native system;
2) Calculating the global entropy of the cloud native system and the node importance factor and the component entropy of the component node;
3) Calculating an observation index ARO according to the global entropy, the node importance factor and the component entropy;
4) And observing the operation condition of each component node in the topological graph of the system through the observation index ARO.
The formula for calculating the observation index ARO is:
ARO i ＝g*n i *e i
wherein, ARO i Is an observation index of the ith component node, g is the global entropy of the cloud native system, n i Node importance factor, e, for the ith component node i Component entropy, g, n, for the ith component node i And e i And obtaining according to the alarm log.
The observation index ARO is based on the alarm log and the information entropy, more aggregated information can be provided for an observer from the system view angle, the real operation condition of the system can be more effectively displayed by combining the global entropy, the node importance factor and the component entropy and considering the multi-dimension from the global and local angles, and meanwhile, the real operation condition of the system can be dynamically changed along with the operation of the cloud native system, the weight does not need to be manually set, the automation is realized, and the efficiency is high.
The global entropy g is calculated as:
wherein p is i Generating an alarm probability for the ith component node;
the global entropy reflects the overall complexity of the cloud native system in a selected time period, the larger the global entropy is, the more complex the whole cloud native system is, for example, the whole cloud native system has problems, each component node generates various alarms, and the system is very chaotic.
Ith component sectionNode importance factor n of a point i The calculation formula of (2) is as follows:
wherein, num i Generating a number of alarms for the ith component node;
the node importance factor represents the frequency of alarm of the component node in a selected time period, and the smaller the frequency is, the larger the node importance factor is.
The calculation formula of the component entropy of the ith component node is as follows:
wherein q is ij The probability of generating the jth alarm by the ith component node;
the entropy of the component reflects the complexity degree of the interior of the node of the component, and the larger the value of the entropy of the component is, the more chaotic the node of the component is represented.
And recording time sequence data of the observation indexes ARO of each component node, and generating a measurement time sequence diagram of each component node.
In the embodiment, the real data of an operation and maintenance system of a certain company is used, 11 components such as 'agent', 'IDC firewall', 'mbox02', 'Mail arbitration', 'supply firewall', 'AD06', 'mbox01', 'IDC agent', 'SH-WSUS', 'AD02', 'AD01' and the like are collected in total, and 460 pieces of alarm information are obtained;
by calculation, g is 2.338,n i Taking the reciprocal of the alarm frequency, the final calculation result is shown in table 1:
TABLE 1 index calculation results Table
| Assembly | Frequency of alarm | Kind of alarm | e i | ARO i |
| mbox01 | 1 | 1 | 0 | 0 |
| |
2 | 2 | 1 | 2.3338 |
| SH- |
4 | 1 | 0 | 0 |
| mbox02 | 13 | 1 | 0 | 0 |
| AD02 | 13 | 5 | 1.8811 | 4.3901 |
| AD01 | 13 | 5 | 1.8811 | 4.3901 |
| Mail arbitration | 22 | 4 | 1.4911 | 3.4799 |
| Firewall for supply section | 30 | 11 | 2.9465 | 6.8765 |
| AD06 | 30 | 5 | 2.001 | 4.6699 |
| IDC firewall | 165 | 8 | 2.4621 | 5.746 |
| Agent | 167 | 13 | 2.7647 | 6.4523 |
From table 1, it can be seen that the greater ARO value of the observed components is the provisioning firewall and the proxy server, as shown in fig. 2 and fig. 3, and by looking at the related alarm logs, the provisioning firewall and the proxy server generate a great amount of report type alarm information during this time, and the component operation condition is more confused during this time.
Example 2
A cloud native system observation system based on information entropy comprises a topology construction module and an index calculation module;
the topology construction module is used for a system topology diagram of the cloud native system, and the system topology diagram comprises each component node of the cloud native system;
the index calculation module comprises a global statistical unit, a weight statistical unit, a local statistical unit, a measurement statistical unit and a time sequence statistical unit;
the global computing unit is used for computing the global entropy g of the cloud native system;
the weight statistical unit is used for counting the node importance factor n of each component node;
the local statistical unit is used for counting the component entropy e of each component node;
the measurement statistical unit is used for calculating an observation index ARO of each component node;
the time sequence statistical unit is used for recording time sequence data of observation indexes ARO of all the component nodes and generating a measurement time sequence diagram of all the component nodes, so that the operation conditions of all the component nodes can be observed visually.
The formula for calculating the observation index ARO is as follows:
ARO i ＝g*n i *e i
wherein, ARO i As an observation index of the ith component node, n i Node importance factor, e, for the ith component node i Component entropy for the ith component node.
The global entropy g is calculated as:
wherein p is i Generating the probability of an alarm for the ith component node;
the global entropy reflects the overall complexity of the cloud native system in a selected time period, the larger the global entropy is, the more complex the whole cloud native system is, for example, the whole cloud native system has problems, each component node generates various alarms, and the system is very chaotic.
Node importance factor n of ith component node i The calculation formula of (c) is:
wherein, num i Generating a number of alarms for the ith component node in a selected time period;
the node importance factor represents the frequency of alarm generation of the component node in a selected time period, and the smaller the frequency is, the larger the node importance factor is.
The calculation formula of the component entropy of the ith component node is as follows:
wherein q is ij The probability of generating the jth alarm by the ith component node;
the component entropy reflects the complexity degree inside the component node, and the larger the value of the component entropy is, the more chaotic the component node is represented.
The embodiment 1 and the embodiment 2 provide an observation method and an observation system of a cloud native system based on information entropy, an observation index ARO is based on an alarm log and the information entropy, more aggregated information can be provided for an observer from a system view angle, multi-dimensional consideration is carried out from the global and local angles through the combination of the global entropy, the node importance factor and the component entropy, the real operation condition of the system can be more effectively displayed, meanwhile, the dynamic change can be realized along with the operation of the cloud native system, the artificial weight setting is not needed, the automation is realized, and the efficiency is high.
The foregoing detailed description of the preferred embodiments of the invention has been presented. It should be understood that numerous modifications and variations can be devised by those skilled in the art in light of the above teachings. Therefore, the technical solutions available to those skilled in the art through logic analysis, reasoning and limited experiments based on the prior art according to the concept of the present invention should be within the scope of protection defined by the claims.
Claims (4)
1. A cloud native system observation method based on information entropy is characterized by comprising the following steps:
constructing a system topological graph comprising each component node of the cloud native system;
observing the operation state of each component node in the topological graph of the system through an observation index ARO;
wherein, the calculation formula of the observation index ARO is as follows:
ARO i ＝g*n i *e i
wherein, ARO i Is an observation index of the ith component node, g is the global entropy of the cloud native system, n i Node importance factor, e, for the ith component node i Component entropy for the ith component node;
the calculation formula of the global entropy g is as follows:
wherein p is i Generating an alarm probability for the ith component node;
the node importance factor n of the ith component node i The calculation formula of (2) is as follows:
wherein, num i Generating alarms for the ith component nodeThe number of the particles;
the calculation formula of the component entropy of the ith component node is as follows:
wherein q is ij Is the probability that the ith component node will generate a class j alarm.
2. The method as claimed in claim 1, wherein time series data of observation indicators ARO of each component node are recorded, and a measurement time series diagram of each component node is generated.
3. A cloud native system observation system based on information entropy is characterized by comprising:
the system comprises a topology construction module, a data processing module and a data processing module, wherein the topology construction module is used for a system topology diagram of the cloud native system, and the system topology diagram comprises each component node of the cloud native system;
the index calculation module comprises a global statistical unit, a weight statistical unit, a local statistical unit and a measurement statistical unit;
the global statistical unit is used for calculating the global entropy g of the cloud native system;
the weight statistical unit is used for counting the node importance factor n of each component node;
the local statistical unit is used for counting the component entropy e of each component node;
the measurement statistical unit is used for calculating an observation index ARO of each component node, and the calculation formula is as follows:
ARO i ＝g*n i *e i
wherein, ARO i Is an observation index of the ith component node, n i Node importance factor, e, for the ith component node i The component entropy of the ith component node is g, and the global entropy of the cloud native system is g;
the calculation formula of the global entropy g is as follows:
wherein p is i Generating an alarm probability for the ith component node;
the node importance factor n of the ith component node i The calculation formula of (2) is as follows:
wherein, num i Generating the number of alarms for the ith component node;
the calculation formula of the component entropy of the ith component node is as follows:
wherein q is ij Is the probability that the ith component node will generate a class j alarm.
4. An information entropy based cloud native system observation system according to claim 3, wherein the index calculation module further includes a time series statistic unit, and the time series statistic unit is configured to record time series data of observation indexes ARO of each component node, and generate a measurement time series diagram of each component node.
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